Fields 2010 Labor Maket Analysis

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Labor Market Analysis for Developing CountriesGary S. FieldsCornell University, [email protected]

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Labor Market Analysis for Developing Countries

Abstract

Tis paper is about analyzing labor markets in developing countries, searching for both improved

understanding and greater policy relevance. Following a ve-part policy evaluation framework, the highlightsof labor markets in developing countries are presented. Teoretical models with multiple sectors andsegments and empirical analysis using dierent kinds of data are then reviewed. A brief concluding sectionaddresses some priority research needs.

Keywords

development, labor markets, policy evaluation

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Labor Mark et Analysis for Developing C ountriesGary S. FieldsCornell University and [email protected] 2010

AbstractThis paper is about analyzing labor markets in developing c ountries, searching for bothimproved un derstanding and greater policy relevance. Following a five-part policyevaluation framework, the highlights of labor markets in developing countries arepresented. Theoretical models with multiple sectors and segm ents and emp irical analysisusing different kinds of data are then reviewed. A brief concluding section addressessome priority research n eeds.

I. IntroductionThis paper is about analyzing labor markets in developing co untries. Developingcountries include m iddle-income countries such as Thailand, Brazil, and So uth Africa aswell as low-income countries such as Pakistan, Haiti, and Tan zania. Developing isitself something of a euph emism, in that although many of these countries are achievingeconomic development, many others are not.The term labor market will be used in this paper to refer to the place where laborservices are bought and sold. In some labor markets, people are paid em ployees, sellingtheir labor services to an employer in exchange for a wage or salary. In other labormarkets, people are self-employed (also called own-account workers), in which they selltheir labor services to themselves. As used here, labor m arket is a comprehensive termincluding both paid employment and self-employment.The focus here is on the labor markets for the poor within these countries. Three billionpeople - almost half of the worlds population - are absolutely poor by internationalstandards (living on less than $2.50 per person per day in purchasing pow er parity units),and essentially all of them live in the developing countries (Chen and R avallion, 2008 ).It is often said that the main and often the sole asset of the poor is their labor. So tounderstand global poverty, one must understand labor markets and labor earnings in thedeveloping world.Consider policy interventions such as the following: E ducating more people in schools.Offering workers a certain type of labor market training. Establishing a workfareprogram. Regulating layoffs and dismissals. Providing thep rwith more to work with,e.g., microcredit. How m ight such prospective policy interventions be analyzed?
mailto:[email protected]:[email protected]


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2The following policy evaluation framework has proven useful in a variety of contexts andforms the basis for the analysis that follows:

Policy instruments Model Outcomes -^Policy evaluation criteria Policy evaluation judgm entThe first step in the framework is to clarify the policy instrument or instruments underconsideration: a law that might be passed, a regulation that might be imposed or removed,a tax, a public expenditure, and the like. This is the action that has been undertaken orthat is under consideration.The second part of the framework is to specify the model. Good models endeavor tocapture the essential aspects of reality while leaving aside the less essential ones. Modelscan be theoretical or empirical; the best ones involve an interplay between the two.Once the policy instrument(s) has/have been specified, the third step is to use the modelto predict the likely outcomes. Sometimes these outcomes are theoretical ones, e.g., thepolicy action in question would increase outcome variable Y if parameter 9is greaterthan a critical value 9* and decrease it otherwise. Other times, the outcomes are empiricalones,e.g., those countries that have achieved more rapid economic growth are those thathave had greater improvements in labor market outcomes.The policy ev aluation is not yet com plete. The fourth step is for the eva luator to specifythe policy evaluation criteria. (I use the plural, because a full-fledged policy evaluationmore often than not relies on multiple policy evaluation criteria rather than a singlecriterion.) The labor market criteria frequently used by labor economists and policymakers today are increasing em ployment and reducing unemploym ent, raising theearnings of those who work so that they and their families can achieve acceptablestandards of living, increasing social protection and providing security against the loss oflabor market earnings, and insuring that core labor standards in the workplace arehonored. Another objective often used in policy evaluation for developing countries isreducing poverty to the maximum extent possible.Fifth and finally, the outcomes are then evaluated in terms of the evaluation criteria andan overall judgmen t is made.Imagine that you are a researcher or a country economist formulating a labor marketpolicy proposal w hich you propose to take to a client country's government. In acompletely-formulated proposal, you would answer the following three questions to thebest of your ability:

What specific labor market objective or objectives are you trying to achieve andby what welfare economic criterion or criteria will you decide if your objective(s) is/arebeing achieved?


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3What theoretical labor market model are you using to analyze the effects of aproposed policy?What is the empirical evidence favoring one view of labor market functioningover another?

These three questions are the ideal, defining what we want to strive for. They are notnecessarily what w e must have.Much useful policy-relevant research has a more limited objective. One such limitedobjective is to get empirical information to help us formulate the right model for aparticular country or market con text. Are wages set according to supply and d eman d, byefficiency wage considerations, or by institutions? D o better-educated workers in acountry earn m ore because of hum an capital created in schools, because of signaling, orbecause of screening? W hy are people working as own-account workers in the informalsecto r, selling their labor to themselve s? Another limited objective is to learn more abouta single outcome or parameter. How elastic is the demand for labor in a country? If morejobs are created in one geographic area, how much migration into that area is induced?How important do firms judge various aspects of the investment climate to be? Policyresearch aimed at these limited objectives is valuable too, and in this paper I presentexamples of em pirical research that has led to interesting policy conclusions.The balance of this paper proceeds as follows. Labor market analysis for developingcountries requires that we look at different issues from the ones typically dealt with byanalysts of developed countries. This different labor market con text is the subject ofSection II. Section III then goes on to talk about the kinds of theoretical models that havebeen used to analyze developing countries labor markets and that m ight be developedfurther. Section IV addresses the different kinds of empirical work that have been and a rebeing done for developing co untries. The paper concludes with some rem arks aboutresearch priorities.

II .The Labor Market C ontext in Developing C ountriesAs readers of this journal well know the g uiding paradigm for the an alysis of labormarkets in the developed countries is the wage labor market. W e picture the typicalemployee going to the same office, store, or factory day after day and earning a wage orsalary that is payable each week or mon th.

As we move dow n the income scale to middle-income countries and then to low-incomecountries, other types of work arrangements gain in im portance. In the developingcountries, steady wage employment of the type found in the developed countries is theexception, not the norm.W orkers in poor countries want jobs that are steady and secu re, pay well, offer benefits,meet labor standards, and offer social protections ones that have been called good


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4jobs. The problem they face is that there are not enough good jobs for all who want themand are capable of performing them.Here are some of the salient features about labor markets in developing countries. Forfurther details, see Fields (forthcoming).The unem ployment rate in developing coun tries is lower than it is in developed countries.Econom ists and other labor market analysts use the terms employment andunemployment in accordance with specific definitions prescribed by the InternationalLabour O rganisation (ILO ). The unemployment rates in the developed eco nomies and theEuropean U nion are above average (8.4%) while those in East Asia and South Asia arebelow average (4.4% and5 .1%respectively) (ILO , 2010 ). The fact that unem ploymentrates are lower in the developing countries is taken by many as a sign that theunemployment rate is a poor measure of labor m arket distress.Earnings levels are very low despite long work hours.In most of Asia, Latin America,and Africa, daily wages are no more than one or two U.S . dollars per day. People areworking more often than not, working very long ho urs. The poor in the developingworld are poor despite working long hours.Not only are incom es low but they are also uncertain.The poor face a triple whammy:low incomes when they are working, irregular and un predictable income flows, and alack of suitable financial tools (Collins, Morduch , Rutherford, and Ruthven, 2 009 ). Theyrespond by managing their m oney carefully.Women are disadvantaged in developing country labor markets.Womens earnings arelower, women s work is more likely to be informal, and women are disproportionately incasua l po sit ions.The com position of employm ent is very different in developing countries from w hat it is indeveloped countries.As compared w ith the developed countries, the developing countrieshave a smaller percentage of people working in offices and factories and a largerpercentage working in agriculture. In the developing co untries, self-employment, own -account work, and unpaid family work are m ore important, and paid employment is lessimportant.The great majority of those working in developing countries work in the private sectornot the public sector. A large majority of those wh o work in the private sector are notregistered w ith the governm ent and therefore do not receive job-related socialprotections.Nine out of ten wo rkers in the developing world are in the private sector. In atypical low-income cou ntry, some 90% of these workers are excluded from governm ent-run economic security programs.Typically the better jobs are in wage employmen t not self-employment. But within wageemploym ent the regular wage jobs are better than casual wage jobs.For these reasons,everybody in developing cou ntries wants a regular wage job.


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The problem the poo r face is that not enough regular wage employm ent is available forall who would like wage jobs and are capable of performing them .Would-be wageemployees could respond to the lack of wage jobs by remaining unemployed andcontinuing to search. However, few do, for the simple reason that they cannot afford to.They find it better to create their own self-employment opportunities. Banerjee and Duflo(2007,p. 162) write: Nothing seems m ore middle class than the fact of having a steadywell-paying job. While there are many petty entrepreneurs among the middle class, mostof them do not seem to be capitalists in waiting. They run businesses, but for the mostpart only because they are still relatively poor and every little bit he lps. If they cou ld on lyfind the right salaried job, they might be quite content to shut their business down.Developing cou ntry labor markets are usually thought to besegmented While definitionsdiffer, the one I prefer has two defining features: first, for workers of any given skilllevel, some jobs are decidedly better than othe rs; and second, access to the better jobs isrationed in the sense that not all who want those jobs and who are capable of performingthem are able to get them. Segmented labor market models will be analyzed in depth inSection III.Not all microenterprise operators and family workers are doing such work involuntarily.Some could be wo rking as wage employees but choose not to for reasons such as wantingto be their own bosses, trying to make it big on their own, and (especially for women)simultaneo usly working and fulfilling child-care respon sibilities. Surely, som e of theself-employed are doing so by choice, but how a large a percentage they are remains unsettledin the literature.Apart from low earnings levels and lack of social protections a large number of jobs aredownright miserable.It is this fact that has led the ILO to organize its efforts around thetheme of Decent Work and to try to close the Decent Work Deficit.What the developing countries have is an employm ent problem that is poverty amongthose who work rather than an unemployment problem.While more than 200 millionpeople were unem ployed in 2009 , some 1,300 million people (i.e., 1.3 billion) belongedto the working poo r, defined as workers who lived in families b elow the internationally-used two dollar a day poverty line. In other words,85%of the worlds poor are working.

III.Theoretical Models with M ultiple Segm ents or SectorsThis section presents models in which the aggregate labor market consists of a smallnumber of labor market segments or sectors linked to one another by actual or potentialmobility of workers and/or firms (Dixit,1973;Basu, 1 997 ; Fields, 2007). The reason weneed different segm ents or sectors is that some parts of the labor m arket operate in aqualitatively different manner from others. The reason we need models that put togetherthese various segments is that conditions in one segment affect and are affected byconditions in other segments.


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6When O ccams Razor is employed and the number of segments is limited to just two, themodels are called dualistic. Here, the two segments w ill be called formal and informal,though they m ight also have been called industry and agriculture, urban and rural,modern and traditional, capitalist and subsistence, wage employment andself-employm ent, covered and non-covered, high-income and low-incom e, and good jobs andbad jobs.The pathbreaking work o n multisector labor market m odels in the context of econom icdevelopment w as done by two Nobel Prize-winning econom ists, W . Arthur Lewis (1954)and Simon K uznets (195 5). Lewis and Kuznets agreed that economic grow th is markedby the gradual shift of wo rkers out of the lower-paying segm ents and into the high er-paying one s. They also agreed that the main development problem is not unemploym entbut rather low incomes in the poorer parts of the econom y. And they agreed to that thesame worker w ould earn quite different amoun ts depending on w here he or she is located.The emergence of hum an capital theory in the 1960 s by Schultz (1961) and B ecker(1964) also earned its developers the Nobel Prize. According to their version of thehuman c apital model, education and training would improve wo rkers skills, enablingthem to wo rk in different economic sectors and earn m ore. Other No bel Prize winnersmodeled the returns to education differently. Signaling models m aintain that w orkers geteducated in order to signal to emp loyers that they (the educated w orkers) are inherentlymore productive than other workers (Spence, 197 3). Screening mod els examine whathappens wh en the educational system certifies which wo rkers are more productive thanothers and the educated workers use their education to move to the front of the queue andbe hired preferentially for jobs for which education is advantageous but not required(Stiglitz, 1 97 5). And rat-race mod els contend that individual i or firm j may be able to getahead by doing m ore of something, but if that very action causes individual m or firm n tofall behind, the social benefits m ay be very much smaller than the private benefits Akerlof,1976).Another early developmen t w as the dua l labor market m odel of Doeringe r and Pio re(1971) .Wachter (1 974, p. 639) summarized the dual labor market model thus:

First, it is useful to dichotomize the economy into a primary and asecondary sector. Second, the wage and employment mechanisms in thesecondary sector are distinct from those in the primary sector. Third,economic mobility between these two sectors is sharply limited, and henceworkers in the secondary sector are essentially trapped the re. Finally, thesecondary sector is marked by pervasive underemploym ent becauseworkers w ho could be trained for skilled jobs at no more than the usualcost are confined to unskilled job s.

Wachter and also Cain (1976) stressed that for labor market dualism to exist, differentwages m ust be paid in different sector to comparable wo rkers. Empirical researchers thenshowed that observationally equivalent workers earned different amounts in differentparts of an eco nom y; see Fields (1980) for an early review . However, skeptics such asRosenzweig (1 988) remained unco nvinced, maintaining that different earnings reflected


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7differences in unmeasured human capital. The alternative to a segmented labor marketmodel is a unitary labor market model, in which all workers with given skills receive thesame wage regardless of wh ich part of the labor m arket they work in. Given thedifferences in labor market outcomes presented in Section II, the unitary view does notseem righ t, and so I continue to find it m ore fruitful to think about mode ls with two or amultiplicity of segm ents.Where a large part of the literature has gone in the intervening years is to continue todevelop the segmented labor market approach. The three main compon ents have been tomode l the formal sector labor m arket, mode l the informal sector labor m arket, and m ode lthe links between the two.On m ode ling the formal sector labor market, options are the supply-de mand mode l withfull market-clearing (as in the standard labor market textbooks), supply and demandmodels with institutional wage determination (as in the textbook m odel of a labor marketwith a m inimum w age), non-market-clearing because of resistance on the supply side ofthe labor market (Bardhan and Rudra,19 8 1 ;Solow, 1 990), non-market-clearing forefficiency wage reasons (K atz, 19 86 ; We iss, 19 90), and a range of other modelsincluding matching m odels (Mortensen and P issarides, 1999), job creation anddestruction m odels (Davis, Haltiwanger, and Schuh , 199 6), ranking models (Blanchardand Diamo nd, 19 94); and imperfect information mod els (Stiglitz, 200 2).On m odeling the informal sector labor m arket, most of the mod els build on the L ewis andKuznets view that people are working informally because they are unable to wo rkformally. My ow n early work (Fields, 197 5) falls into this category, as does Harberger(1971).But it was later recognized that there is a fundamental duality within the informalsector, whereby some people w ork in a lower tier because they can d o no better, whileothers work in an upper tier into which entry is restricted because of human capital andfinancial cap ital requiremen ts (Fields, 1990). Some analysts maintain that in Mexico andother Latin American countries, self-employment in the informal sector may be moreattractive for a majority of workers compared to work as wage employees in the formalsector (Maloney,2003,200 4; World Bank, 20 07). Opinion remains divided on how manypeople are working in each tier.Analysts have also modeled intersectoral linkages in a variety of ways. One is a no n-competing grou ps model in which individuals belong to one labor market segment oranother, and they cannot or will not switch from one to another (as in many humancapital m odels). A second is an integrated labor m arket model which starts with two ormore sectors but assumes that all of the equilibrating forces that apply to a single labormarket with market-clearing also apply to a labor market with a multiplicity of sectors, sothat wages equalize across sectors; nearly all international trade models have such a labormarket specification (Krugman and Obstfeld, 200 3). A third way of modelingintersectoral linkages is the crowding model, which assum es that any worker w ho is notemployed in the high wage part of the economy takes up emp loyment in the low w agepart of the econom y; the Lewis and Kuznets m odels mentioned above can be recognizedas crowding m odels. A fourth specification is that one sector maintains higher w ages than


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8another, but the two sectors are linked via workers job search behavior, such that inequilibrium the expected wages associated with the two sectors are equal to one another(Harris and T oda ro, 1970 ). Finally, a fifth type of mo del posits that workers ch ooseoccupations which maximize their current and future returns, but because of imperfectcapital markets, occupations that require high levels of investment cannot be entered bypersons with low initial wealth. The distribut ions of w orke rs in different occupationsevolve over time as individuals invest their time and money t o increase their wealth orthe wealth of their children (Banerjee and Newman, 1993).Where the field is at present is that we have many multisector models incorporating avariety of features of developing country labor m arkets. The Harris-Todaro m odel wasformulated as a stylization of the Kenyan economy. But for researchers seekingstylizations of India, South Africa, Brazil, or other developing economies, it remains forappropriate labor market models to be developed to fit each countrys particular set ofconditions. For further details, see Fields (2007 ).

IV. Empirical R esearch on Labor M arkets and Labor Market P oliciesIn this section, I discuss five kinds of data that have been used to analyze developingcountries labor m arkets micro cross-sectional da ta, panel data, aggregate cross-sectional data, time series da ta, and experimental data and present exam ples ofempirical studies that have used e ach.A. Micro cross-sectional data.Micro data sets exist for most developing countries. A typical issue of any of the leadingdevelopment economics journals contains a number of papers using such data sets.

Studies analyzing micro data enable researchers to find out what makes a difference atthe level of the individual worke r or the individual house hold. This type of data analysisis at the core of most contemporary research studies on labor markets, and the literatureusing such data is correspondingly h uge .The question these studies typically answer is, which individuals or which firms havehigher levels of the dependent variable Y? More specifically, the regression coefficientscan be understood th us: when we comp are those individuals or firms for which anindependent variable X is one unit higher, on average how m uch higher is Y?It is well-known that the answers given by simple tabulations and ordinary least squaresregressions can be problematical even within their ow n frame of reference. The tw o mainproblems are correlation but not causation and the p roblem of bias in the estimatedcoefficients. In day-to-day labor economics, a single solution is commonly offered toboth problem s, namely, instrumental v ariables estimation. Deaton (1 997 , 2000) haspresented the advantages of instrumental variables method s as well as their limitations;see also Bound, Jaeger, and Baker (1995) and Staiger and Stock (1 997 ). As Bo und,Jaeger, and Baker put it so graphically in the title of the working paper version of their


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9paper, the cure can be worse than the disease. That ob servation d oes not, however, stoplabor econom ists from instrumenting at virtually every oppo rtunity.Another way of dealing with problems in micro data analysis is to adopt a regressiondiscontinuity d esign. Many such studies are appearing using this method as well. SeeImbens and Lemieux (20 07) for a review.But less attention is paid to another problem: that the micro level results typically makecertain assump tions about m arket structure that may not hold in the circumstancesanalyzed. For example, when m ore people are educated, how do we know that the new ly-educated w ill be employed at all, let alone in jobs where they will gain as m uch inearnings as the differential that we now observe between more-educated and less-educated w orkers? Because of this problem, when the results of such regressions are usedto draw policy implications for example, about educating more people - they may notbe implications at all.

Notwithstanding the p ossible misuses of micro data regressions, it would be foolhardy inthe extreme to dismiss them. Such regression results are very useful information to hav e.My point is that we need to be suitably modest in drawing po licy inferences from them.

B . Panel data.Panel data analysis is a relatively new and promising approach in developing countries.Panel studies follow the same individuals or families over time. Such studies enabledevelopment processes to be analyzed in ways that analyses of comparable cross-sectionscannot. Two areas in which a great deal of policy-relevant know ledge has been gainedare the effects of policy interventions on treated individuals and the determinants ofincome and ea rnings m obility.On the first topic, studies of policy interventions estimate such measures as the averagetreatment effect, the average treatment effect for those treated, and the distribution oftreatment effects for different individuals. Heckman (2001) and Ravallion (2008) areamong those who present com prehensive overviews of the relevant issues in estimatingsuch magnitudes.Empirical research of this type has proved informative in the context of developingcountries. One example is the study by Kug ler (2005) of a policy change in Colom bia,which replaced its earlier system of severance paymen ts with a new system of mandatedseverance paym ents savings accounts (SPSA s). Kuglers question was w hether part ofthis obligation was shifted to workers in the form of lower wages. Using a difference-in-difference-in-difference estima tor, she concluded tha t 60-8 0% of the total SPS Acontributions were shifted to wo rkers in the form of lower w ages, thus disproving theview held by some that such mand ates would be free to the work ers.Another example of using panel data to estimate the effects of a labor market policychange is the study by Ravallion et al. (2005) of Argentinas workfare program , known in


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10Spanish as Trabajar (To Wo rk). Using a difference-in-difference-in-difference estima tor,the authors concluded that workers suffered large earnings losses from retrenchment andexperienced sizeable income gains from participating in Trabajar, while those who leftthe program lost considerable amounts relative to those who stayed.Another topic that has been explored fruitfully using pane l data in developing c ountries isincome and earnings mobility. (Income denotes income from all sources, whileearnings denotes income from w age employm ent, salaried em ploymen t, andself-employment only.) One question that has been asked is, how have econom ic growth andeconomic decline affected income or earnings changes for different groups in thepo pulation: men v s. women, better-educated vs. less-educated, initially h igh earners vs.initially lower earne rs, and so on? F ields et al. (2007) studied this question for Argentina,M exico , and Venezuela. They found that notwithstanding the high and generally risingearnings inequality in all three countries, both in times of economic growth and in timesof economic d ecline, the largest average earnings gains in currency un its (pesos in thecase of A rgentina and Me xico, bolivares in the case of Venezuela) were experienced bythose who were initially at the bottom end of the earnings distribution, and they alsofound that a key variable explaining earnings chan ge was the change in employmentstatus between formally employed, informally employed, and unemployed.In addition to the work done on earnings m obility, there also is a literature on income andconsumption mobility in developing countries. See the work of the Chronic PovertyResearch Centre(www.chronicpoverty.org)on countries around th e world.

C. Aggregate cross-sectional data.The analysis of aggregate data is a useful and o ften-neglected starting point for policyanalysis, permitting answ ers to questions such as the following. D o w orkers participateproportionally in econom ic growth? M ore generally, how d o labor market co nditionschange during times of growth and decline? How d o the outcomes in one countrycompare with those in another? How do changes in labor market outcomes for onepopulation sub-group compare with those of another? Is economic grow th jobless, and ifso ,under what circumstances?The general result that has come out of such aggregate data analysis for a limited numberof countries is that econom ic growth has generally led to improved conditions in labormarkets as gauged by ind icators such as the rate of u nemploym ent, com position ofemploymen t, real earnings levels, earnings inequality, and pov erty. Conversely, whensuch labor market aggregates have not im proved, typically it is because economic growthhas not taken place.This line of research suggests that to improve labor m arket conditions for workers ingeneral and for the poor in particular, economic grow th should be sou ght. The pessimisticview - that workers have been left out or even made poorer w hen economic grow th hastaken place is not suppo rted by the data as a generalization.
http://www.chronicpoverty.org/http://www.chronicpoverty.org/http://www.chronicpoverty.org/


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D .Time series data.The simplest kind of time series data is that which follows given variables within acountry over time . Time series data allow for the determinants of changes over time to bemodeled explicitly. For example, the wage elasticity of formal sector labor demand inSouth Africa w as estimated u sing such data; Fields, Leibbrandt, and Wakeford (2000)present their own and earlier estimates. Despite differences in time periods andestimation method s, these studies concurred that the range of estimates was o n the orderof -0.4 to -0.6 certainly not ze ro. This finding led policy-mak ers in that country torecognize that the push by the Congress of South A frican Trade Unions for higher wageswould likely result in reduced formal sector employment.A m ore comprehensive kind of data com bined time series with cross sections that is,time series are presented for a number of countries or for groups or regions withincountries. Such is the case for a study of the effects of labor market regulations in India.Besley and Burgess (2004) used time series data for each of Indias states to assess theeffects of am ending Indias Industrial Disputes Act in an allegedly pro-worker direction.Using sophisticated econometric m ethods, they estimated that the legislation had led toan informalization of the economy and to higher urban poverty than would have been thecase without the amendm ents. Like Besley and Bu rgess, Ahsan and Pags (2009) alsoused cross-section time-series m etho ds. Their main result wa s that de jure lega lamendments and de facto use of contract labor hurt formal sector workers and imposedsubstantial costs on society.

E. Experimental data.A burgeoning literature in development economics uses randomized field experiments tofind out what works in practice. Here are three examples of the kinds of lessons to comefrom this kind of literature.First, to address the problem of teacher absenteeism in India, teachers in rando mly-selected schools were given a camera with a tamper-proof date and time function and toldto take a picture of him/herself and the students at the beginning and end of each schoolday. Teachers were offered a bonus based on the number of days they actually came toschool. The evaluation results (Duflo and Hanna, 20 05) showed that the absence rate inthe treatment schools (22%) was half of that in the control schools (42%).A second examp le of randomized treatment in the labor market is the Co lombianworkfare program Em pleo en Accin (Emp loyment in Action). In an evaluation of thisprogram, Attanasio, Meghir and Vera-Hernandez (2007 ) found that the programpositively influenced the number of hours worked and bo th individual and householdlabor income. Household incom e increased more than individuals incom e, indicatingthat the program had some positive externality on the other members of the household.A third application of experimental methods is in the evaluation of job training in LatinAm erica. In one study, Card et al. (2007) studied the Dominican R epublic and found that


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12job training had no significant impact on the likelihood of having a job, but it did have amarginally significantly impact for wages and also for having health insurance coverageconditional on being employed. A nother randomized ev aluation of a training program isreported by Attanasio, Kugler and M eghir (2007) for Colomb ia. Contrary to Card et alsfindings for the Dom inican Repu blic, this study found that in Colombia jo b training hadlarge, widespread effects on women, but fewer and less pronounced effects on men.Literally hundreds of experimental studies have been completed, and a great many othersare under way. Overviews of this literature are presented in Duflo (2006), Banerjee(2007),and www.povertyactionlab.org.

V. Concluding RemarksIn this concluding section, I briefly highlight four priority areas for future researchrelating to the issues raised in this pape r.

The first is to understand b etter how people in developing co untries are working and w hythey are doing what they are doing. W hile more descriptive analysis would beinformative, even more valuable for our understanding would be knowledge about whatis enabling some w orkers to obtain better labor market outcomes and what is constrainingothers from m aking such gains.The second is the need for empirically-grounded theoretical labor market models that canbe used in the formulation of policy. There is some value in developing single-sectormodels and representative agent models, but it would be more helpful to have multi-sector models in which labor markets are segmented, incorporating the key features oflabor markets in the country being an alyzed.The third is the need for sector-level and market-level analysis of various o utcom es.Analyses at these levels are likely to be more illuminating than w ould additional m icro-level regressions.And fourth is the need for in-depth empirical analysis on which particular policyinterventions m atter or do not m atter. We can gain su ch knowledge from statistical andeconometric studies, from experimental studies, or from a m ix of the tw o. What we needmost are additional studies that tell us specifically what has made a positive differenceand what has not for example, that student performance and hence workers skills canbe raised by improving teacher quality but not b y reducing class sizes. Not as importantare more studies that say that those individuals/households/workers/students with more ofcharacteristic X have such-and-such higher value of outcome variable Y in country Z.Much remains to be learned ab out these and other issues such as child labor,entrepreneurship, employment and w orkplace protection, and many other o thers. Thispaper will have succeeded if it has suggested new ways of thinking to those readers whoalready work on developing countries labor markets and stimulated others to want towork in this area.
http://www.povertyactionlab.org/http://www.povertyactionlab.org/


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13References

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